Second, due to different development levels and urban construction styles, different cities have their own specific urban layouts, which lead to distinctive height distribution. This results in a severe cross-city domain shift. Such domain shifts are very common in real-world remote sensing applications.
However, existing methods usually train and test MHE models using unbiased datasets [1]}. Given this problem, in this work we take the city biases into consideration by proposing a new cross-city transfer benchmark dataset. We also benchmark some baseline results to provide useful insights on designing height estimation models in a cross-dataset setting.
